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Several studies showed that assessing levels of specific circulating microRNAs (miRNAs) is a non-invasive, rapid, and accurate method for diagnosing diseases or detecting alterations in physiological conditions. We aimed to identify a serum miRNA signature to be used for the diagnosis of tuberculosis (TB). To account for variations due to the genetic makeup, we enrolled adults from two study settings in Europe and Africa. The following categories of subjects were considered: healthy (H), active pulmonary TB (PTB), active pulmonary TB, HIV co-infected (PTB/HIV), latent TB infection (LTBI), other pulmonary infections (OPI), and active extra-pulmonary TB (EPTB). Sera from 10 subjects of the same category were pooled and, after total RNA extraction, screened for miRNA levels by TaqMan low-density arrays. After identification of “relevant miRNAs”, we refined the serum miRNA signature discriminating between H and PTB on individual subjects. Signatures were analyzed for their diagnostic performances using a multivariate logistic model and a Relevance Vector Machine (RVM) model. A leave-one-out-cross-validation (LOOCV) approach was adopted for assessing how both models could perform in practice. The analysis on pooled specimens identified selected miRNAs as discriminatory for the categories analyzed. On individual serum samples, we showed that 15 miRNAs serve as signature for H and PTB categories with a diagnostic accuracy of 82% (CI 70.2–90.0), and 77% (CI 64.2–85.9) in a RVM and a logistic classification model, respectively. Considering the different ethnicity, by selecting the specific signature for the European group (10 miRNAs) the diagnostic accuracy increased up to 83% (CI 68.1–92.1), and 81% (65.0–90.3), respectively. The African-specific signature (12 miRNAs) increased the diagnostic accuracy up to 95% (CI 76.4–99.1), and 100% (83.9–100.0), respectively. Serum miRNA signatures represent an interesting source of biomarkers for TB disease with the potential to discriminate between PTB and LTBI, but also among the other categories.

RTS,S/AS01(E) integrated in the EPI showed a favorable safety and immunogenicity evaluation. Trial registration. ClinicalTrials.gov identifier: NCT00436007 . GlaxoSmithKline study ID number: 106369 (Malaria-050).

In sub-Saharan Africa (SSA), epidemiological data for chronic kidney disease (CKD) are scarce. We conducted a prospective cross-sectional study including 952 patients in an outpatient clinic in Tanzania to explore CKD prevalence estimates and the association with cardiovascular and infectious disorders. According to KDIGO, we measured albumin-to-creatinine ratio and calculated eGFR using CKD-EPI formula. Factors associated with CKD were calculated by logistic regression. Venn diagrams were modelled to visualize interaction between associated factors and CKD. Overall, the estimated CKD prevalence was 13.6% (95% CI 11–16%). Ninety-eight patients (11.2%) (95% CI 9–14%) were categorized as moderate, 12 (1.4%) (95% CI 0–4%) as high, and 9 (1%) (95% CI 0–3%) as very high risk according to KDIGO. History of tuberculosis (OR 3.75, 95% CI 1.66–8.18; p = 0.001) and schistosomiasis (OR 2.49, 95% CI 1.13–5.18; p = 0.02) were associated with CKD. A trend was seen for increasing systolic blood pressure (OR 1.02 per 1 mmHg, 95% CI 1.00–1.03; p = 0.01). Increasing BMI (OR 0.92 per 1kg/m2, 95% CI 0.88–0.96; p = <0.001) and haemoglobin (OR 0.82 per 1g/dL, 95% CI 0.72–0.94; p = 0.004) were associated with risk reduction. Diabetes was associated with albuminuria (OR 2.81, 95% CI 1.26–6.00; p = 0.009). In 85% of all CKD cases at least one of the four most common factors (hypertension, diabetes, anaemia, and history of tuberculosis or schistosomiasis) was associated with CKD. A singular associated factor was found in 61%, two in 14%, and ≥3 in 10% of all CKD cases. We observed a high prevalence estimate for CKD and found that both classical cardiovascular and neglected infectious diseases might be associated with CKD in a semi-rural population of SSA. Our finding provides further evidence for the hypothesis that the “double burden” of non-communicable and endemic infectious diseases might affect kidney health in SSA.